Applied Physics

Applied Physics — AI Data Trainer About The Role What if your expertise in quantum mechanics, electrodynamics, and thermodynamics could directly shape how AI understands the physical world? We're looking for PhD-level Applied Physicists to stress-test cutting-edge Large Language Models — exposing the gaps in their physical reasoning and helping build AI that truly respects the fundamental laws of the universe. This is a fully remote, flexible contract role designed for researchers and academics who want to apply their deep domain knowledge to one of the most consequential technology challenges of our time. No prior AI experience required. Organization: Alignerr Type: Hourly Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Design Advanced Physics Problems — Craft PhD-qualifying-exam-level problems requiring multi-step logical reasoning, mathematical derivation, and deep physical intuition across domains like quantum mechanics, electromagnetism, and thermodynamics Author Rigorous Solutions — Produce precise, step-by-step "golden responses" with flawless physical constants, unit conversions, and logical structure that serve as ground-truth benchmarks Audit AI Reasoning — Evaluate AI-generated simulations, proofs, and derivations for physical consistency; identify where models "hallucinate" physics that violates first principles Refine Model Behaviour — Provide structured, expert feedback that helps AI systems develop physics-informed reasoning — correctly applying boundary conditions, conservation laws, and symmetry constraints Who You Are Hold a PhD (completed or near completion) in Applied Physics, Physics, Engineering Physics, or a closely related field Possess mastery across the core pillars: Classical Mechanics, Electrodynamics, Statistical Mechanics, and Quantum Mechanics Can explain complex physical phenomena and rigorous mathematical derivations in clear, well-structured English Obsessively precise — you notice a wrong unit, a sign error, or a broken logical step immediately Self-directed and comfortable working independently in an asynchronous environment No prior AI or machine learning experience required Nice to Have Experience with data annotation, dataset quality review, or scientific evaluation frameworks Proficiency with computational tools such as Python (NumPy/SciPy), MATLAB, or COMSOL Background in research-level benchmarking or academic problem-set design Why Join Us Work on frontier AI projects in partnership with the world's leading AI research labs Fully remote and flexible — structure your hours around your existing commitments Apply your expertise to problems that genuinely matter — shaping how AI reasons about physical reality Freelance autonomy: no bureaucracy, no commute, just meaningful, intellectually engaging work Potential for ongoing contract extension as new projects launch

Design advanced PhD-level physics problems and author rigorous step-by-step solutions to serve as ground-truth benchmarks for AI. Audit AI-generated proofs and simulations to identify hallucinations and refine the model's physical reasoning capabilities.

Requires a PhD in Applied Physics, Physics, Engineering Physics, or a related field with mastery of core physics pillars. Candidates must be obsessively precise in mathematical derivations and capable of explaining complex phenomena in clear English.

• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Quantum Mechanics
• Electrodynamics
• Thermodynamics
• Classical Mechanics
• Statistical Mechanics
• Mathematical Derivation
• Data Annotation
• Python
• NumPy
• SciPy
• MATLAB
• COMSOL
• Scientific Evaluation
• Problem-set Design
• Physical Reasoning
• Technical Writing

• Science & Research
• Technology
• Engineering
• Data & Analytics
• Software

Minimum education

Master’s degree

Minimum experience

5+ years

Apply by

Sep 23, 2026

Posting language

English

Working hours

40 hours per week

Location requirements

Country, Vancouver, British Columbia, Canada

Seniority

Mid-Senior level